from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	1.969990	0.148898	NaN	0.000406	0.001970	brute	-1	1	0.663	0.194904	0.006901	0.687	10.107465	10.113800	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.921108	0.028251	NaN	0.000274	0.002921	brute	-1	5	0.757	0.194911	0.001272	0.742	14.986918	14.987238	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.210127	0.010942	NaN	0.000362	0.002210	brute	1	100	0.882	0.242505	0.003347	0.875	9.113756	9.114624	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.020651	0.000483	NaN	0.000039	0.020651	brute	1	100	1.000	0.008281	0.000219	0.000	2.493897	2.494773	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.907368	0.040011	NaN	0.000275	0.002907	brute	-1	100	0.882	0.239126	0.001434	0.875	12.158308	12.158526	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.024246	0.002924	NaN	0.000033	0.024246	brute	-1	100	1.000	0.008427	0.000831	0.000	2.877169	2.891140	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.184117	0.008282	NaN	0.000366	0.002184	brute	1	5	0.757	0.194345	0.001629	0.742	11.238362	11.238757	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.191104	0.003320	NaN	0.000672	0.001191	brute	1	1	0.663	0.194272	0.002326	0.687	6.131110	6.131550	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.730465	0.035290	NaN	0.000009	0.001730	brute	-1	1	0.896	0.030833	0.000936	0.967	56.123240	56.149091	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.772486	0.027425	NaN	0.000006	0.002772	brute	-1	5	0.922	0.031989	0.001134	0.974	86.669797	86.724230	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	2.101553	0.004865	NaN	0.000008	0.002102	brute	1	100	0.929	0.071073	0.002416	0.975	29.569097	29.586170	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.771628	0.027693	NaN	0.000006	0.002772	brute	-1	100	0.929	0.070786	0.001922	0.975	39.154852	39.169284	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	2.079044	0.009464	NaN	0.000008	0.002079	brute	1	5	0.922	0.031891	0.000830	0.974	65.191640	65.213706	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.072272	0.005762	NaN	0.000015	0.001072	brute	1	1	0.896	0.030387	0.000222	0.967	35.287020	35.287958	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.953	0.0	-1	1	0.049	0.004	0.233	0.233	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.891	0.0	-1	5	0.048	0.000	0.240	0.240	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.903	0.0	1	100	0.049	0.000	0.239	0.239	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.851	0.0	-1	100	0.049	0.000	0.239	0.239	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.892	0.0	1	5	0.048	0.000	0.242	0.242	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.900	0.0	1	1	0.048	0.000	0.239	0.239	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.330	0.0	-1	1	0.009	0.000	0.522	0.522	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.326	0.0	-1	5	0.009	0.000	0.524	0.524	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.326	0.0	1	100	0.009	0.000	0.525	0.525	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.328	0.0	-1	100	0.010	0.000	0.511	0.511	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.306	0.0	1	5	0.009	0.000	0.568	0.568	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.328	0.0	1	1	0.009	0.000	0.514	0.515	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.970	0.149	0.000	0.002	-1	1	0.195	0.007	10.107	10.114	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.002	0.000	0.022	-1	1	0.008	0.000	2.743	2.744	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.921	0.028	0.000	0.003	-1	5	0.195	0.001	14.987	14.987	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	5	0.008	0.000	2.885	2.886	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.210	0.011	0.000	0.002	1	100	0.243	0.003	9.114	9.115	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	100	0.008	0.000	2.494	2.495	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.907	0.040	0.000	0.003	-1	100	0.239	0.001	12.158	12.159	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.003	0.000	0.024	-1	100	0.008	0.001	2.877	2.891	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.184	0.008	0.000	0.002	1	5	0.194	0.002	11.238	11.239	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	5	0.008	0.000	2.545	2.545	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.191	0.003	0.001	0.001	1	1	0.194	0.002	6.131	6.132	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	1	0.008	0.000	2.329	2.330	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.730	0.035	0.000	0.002	-1	1	0.031	0.001	56.123	56.149	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.000	0.006	-1	1	0.001	0.000	7.693	7.740	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.772	0.027	0.000	0.003	-1	5	0.032	0.001	86.670	86.724	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.003	0.000	0.007	-1	5	0.001	0.000	9.477	9.562	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.102	0.005	0.000	0.002	1	100	0.071	0.002	29.569	29.586	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.914	3.932	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.772	0.028	0.000	0.003	-1	100	0.071	0.002	39.155	39.169	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.000	0.006	-1	100	0.001	0.000	7.469	7.509	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.079	0.009	0.000	0.002	1	5	0.032	0.001	65.192	65.214	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.400	4.414	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.072	0.006	0.000	0.001	1	1	0.030	0.000	35.287	35.288	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.658	2.675	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.787703	0.941130	NaN	0.000102	0.000788	kd_tree	-1	1	0.929	0.107080	0.003512	0.910	7.356233	7.360189	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.068765	0.363824	NaN	0.000075	0.001069	kd_tree	-1	5	0.946	0.189717	0.003287	0.941	5.633471	5.634316	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.509321	0.446831	NaN	0.000015	0.005509	kd_tree	1	100	0.951	0.576296	0.004887	0.940	9.559889	9.560233	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.285155	0.279967	NaN	0.000024	0.003285	kd_tree	-1	100	0.951	0.573701	0.007064	0.940	5.726244	5.726678	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.701067	0.310033	NaN	0.000047	0.001701	kd_tree	1	5	0.946	0.195848	0.002602	0.941	8.685640	8.686406	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.934033	0.304402	NaN	0.000086	0.000934	kd_tree	1	1	0.929	0.106195	0.007010	0.910	8.795449	8.814591	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.027836	0.012823	NaN	0.000575	0.000028	kd_tree	-1	1	0.891	0.000438	0.000045	0.879	63.521387	63.855657	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.024767	0.001410	NaN	0.000646	0.000025	kd_tree	-1	5	0.911	0.000712	0.000024	0.905	34.784349	34.803503	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.037197	0.001582	NaN	0.000430	0.000037	kd_tree	1	100	0.894	0.005176	0.000126	0.917	7.186030	7.188153	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.042963	0.005407	NaN	0.000372	0.000043	kd_tree	-1	100	0.894	0.006148	0.001438	0.917	6.988549	7.177246	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.021961	0.000242	NaN	0.000729	0.000022	kd_tree	1	5	0.911	0.000712	0.000047	0.905	30.838644	30.906630	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.020320	0.000222	NaN	0.000787	0.000020	kd_tree	1	1	0.891	0.000465	0.000045	0.879	43.675119	43.881440	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.205	0.027	0.025	0.0	-1	1	0.784	0.059	4.090	4.101	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.899	0.040	0.021	0.0	-1	5	0.763	0.017	5.113	5.114	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.940	0.064	0.020	0.0	1	100	0.742	0.005	5.313	5.313	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.893	0.059	0.021	0.0	-1	100	0.777	0.016	5.010	5.012	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.848	0.074	0.021	0.0	1	5	0.747	0.010	5.148	5.148	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.901	0.050	0.021	0.0	1	1	0.782	0.065	4.988	5.005	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.017	0.0	-1	1	0.003	0.002	0.269	0.326	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.025	0.0	-1	5	0.001	0.001	0.445	0.581	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	100	0.001	0.001	0.490	0.592	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	100	0.001	0.000	0.637	0.637	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	5	0.001	0.000	0.653	0.654	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	1	0.001	0.000	0.617	0.617	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.788	0.941	0.000	0.001	-1	1	0.107	0.004	7.356	7.360	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	9.539	9.974	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.069	0.364	0.000	0.001	-1	5	0.190	0.003	5.633	5.634	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.458	7.777	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.509	0.447	0.000	0.006	1	100	0.576	0.005	9.560	9.560	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.044	4.155	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.285	0.280	0.000	0.003	-1	100	0.574	0.007	5.726	5.727	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.097	6.325	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.701	0.310	0.000	0.002	1	5	0.196	0.003	8.686	8.686	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.906	4.035	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.934	0.304	0.000	0.001	1	1	0.106	0.007	8.795	8.815	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.945	4.160	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.028	0.013	0.001	0.000	-1	1	0.000	0.000	63.521	63.856	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	28.412	30.072	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	0.001	0.000	-1	5	0.001	0.000	34.784	34.804	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	26.323	27.415	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.037	0.002	0.000	0.000	1	100	0.005	0.000	7.186	7.188	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.722	7.039	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.043	0.005	0.000	0.000	-1	100	0.006	0.001	6.989	7.177	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	0.000	0.002	-1	100	0.000	0.000	22.651	23.394	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.000	0.001	0.000	1	5	0.001	0.000	30.839	30.907	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.923	7.269	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	1	0.000	0.000	43.675	43.881	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	7.025	7.328	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.575	0.069	30	0.028	0.0	random	0.438	0.032	1.311	1.315	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.640	0.016	30	0.025	0.0	k-means++	0.470	0.032	1.363	1.366	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.897	0.221	30	0.136	0.0	random	2.717	0.037	2.170	2.170	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.070	0.012	30	0.132	0.0	k-means++	2.857	0.014	2.125	2.125	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	random	0.0	0.0	7.221	13.132	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	9.048	13.674	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	k-means++	0.0	0.0	12.384	14.092	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	13.091	13.874	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.397	0.000	random	0.0	0.0	7.468	7.853	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	12.985	13.311	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.422	0.000	k-means++	0.0	0.0	6.771	7.165	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	12.284	12.613	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.002002	0.000107	20	0.007993	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000507	0.000062	-0.000965	3.946182	3.975261	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.002085	0.000250	20	0.007673	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000492	0.000051	-0.000750	4.235296	4.257751	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002795	0.000167	20	0.286235	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001122	0.000088	0.293767	2.490328	2.497911	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002932	0.000294	20	0.272867	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001121	0.000111	0.256968	2.614597	2.627344	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.086	0.001	20	0.002	0.0	random	0.030	0.001	2.824	2.826	See	See
3	KMeans_short	fit	10000	10000	2	0.246	0.003	20	0.001	0.0	k-means++	0.094	0.001	2.621	2.621	See	See
6	KMeans_short	fit	10000	10000	100	0.221	0.003	20	0.036	0.0	random	0.124	0.003	1.774	1.774	See	See
9	KMeans_short	fit	10000	10000	100	0.658	0.012	20	0.012	0.0	k-means++	0.356	0.002	1.848	1.848	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.001	0.0	3.946	3.975	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	0.000	0.0	13.969	14.375	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.000	0.0	4.235	4.258	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	13.791	14.235	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.286	0.000	random	0.001	0.0	2.490	2.498	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	0.000	0.0	10.664	10.819	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.273	0.000	k-means++	0.001	0.0	2.615	2.627	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	k-means++	0.000	0.0	9.480	9.601	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000393	0.000338	[20]	2.036946	3.927449e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000900	0.001705	0.55	0.436589	0.935433	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001774	0.000169	[26]	4.509757	1.773932e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.005579	0.000721	0.28	0.317989	0.320634	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.311	0.401	[20]	0.071	0.000	1.937	0.031	5.840	5.840	See	See
3	LogisticRegression	fit	1000	1000	10000	0.912	0.504	[26]	0.088	0.001	0.794	0.024	1.149	1.149	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.037	0.0	0.001	0.002	0.437	0.935	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.014	0.0	0.000	0.000	0.375	0.387	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.510	0.0	0.006	0.001	0.318	0.321	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.049	0.0	0.002	0.000	0.044	0.044	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.009989	0.000153	NaN	8.008992	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.016514	0.000188	0.122191	0.604867	0.604907	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.191	0.003	0.419	0.0	0.195	0.001	0.978	0.978	See	See
3	Ridge	fit	1000000	1000000	100	1.189	0.089	0.673	0.0	0.340	0.311	3.496	4.736	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	8.009	0.0	0.017	0.0	0.605	0.605	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.194	0.0	0.000	0.0	0.685	0.738	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	4.956	0.0	0.000	0.0	0.456	0.737	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.0	0.605	0.638	See	See